UCI Adult Income Dataset - Exploratory and Descriptive Analysis

Author
Affiliation

Nyiramana Annick

Junior Data Analyst

Published

June 25, 2025

In this notebook, we carry out an in-depth exploratory and descriptive analysis of the UCI Adult Income Dataset, a widely used dataset for income prediction tasks based on individual demographic and employment attributes.

This phase of analysis is essential for uncovering patterns, detecting potential biases, and gaining intuition about the dataset’s structure before applying any modelling procedures. We examine the distribution of key numerical and categorical variables, investigate relationships between demographic features and income levels, and use visualizations to summarize insights. Particular focus is placed on income disparities across **age groups**, **geographical regions**, **races**, and **education-occupation combinations**, helping lay a solid foundation for downstream modeling and policy-relevant interpretation.

We begin our analysis by importing the core Python libraries required for **data handling**, **numerical computation**, **visualization**, and **directory management**:

- `pandas`: Enables efficient manipulation, filtering, and aggregation of structured tabular data, forming the backbone of our analysis pipeline.

- `numpy`: Provides support for fast numerical operations, array-based computation, and statistical routines.

- `os`: Facilitates interaction with the file system, allowing us to construct flexible and portable directory paths for data and output management.

- `plotly.express`: A high-level graphing library that enables the creation of interactive, publication-quality visualizations, which we use extensively to uncover patterns and present insights throughout the notebook.

Code
# Import libraries 
import os
import pandas as pd
import numpy as np
import plotly.express as px
Code
import plotly.io as pio
pio.renderers.default = 'notebook'

Define and Create Directory Paths

To ensure reproducibility and organized storage, we programmatically create directories if they don’t already exist for:

- raw data
- processed data
- results
- documentation

These directories will store intermediate and final outputs for reproducibility.

Code
# Get working directory 
current_dir = os.getcwd()

# Go one directory up to the root directory 
project_root_dir = os.path.dirname(current_dir)
data_dir = os.path.join(project_root_dir, 'data')
raw_dir = os.path.join(data_dir,'raw')
processed_dir = os.path.join(data_dir,'processed')
# Define paths to results folder 
results_dir = os.path.join(project_root_dir,'results')
# Define paths to docs folder 
docs_dir = os.path.join(project_root_dir,'docs') 

#Create directories if they do not exist 
os.makedirs(raw_dir,exist_ok= True)
os.makedirs(raw_dir,exist_ok= True)
os.makedirs(raw_dir,exist_ok= True)
os.makedirs(raw_dir,exist_ok= True)
os.makedirs(results_dir, exist_ok=True)
os.makedirs(processed_dir, exist_ok=True)

Loading the Cleaned Dataset

We load the cleaned version of the UCI Adult Income Dataset from the processed data directory into a Pandas DataFrame. The `head(10)` function shows the first ten records, giving a glimpse into the data columns such as `age`, `workclass`, `education_num`, etc.

Code
adult_data_filename= os.path.join(processed_dir, "adult_cleaned.csv")
adult_df = pd.read_csv(adult_data_filename)
adult_df.head(10)
age workclass fnlwgt education_num marital_status relationship race sex capital_gain capital_loss hours_per_week income education_level occupation_grouped native_region age_group
0 39 government 77516 13 single single white male 2174 0 40 <=50k tertiary white collar north america 36-45
1 50 self-emp-not-inc 83311 13 married male spouse white male 0 0 13 <=50k tertiary white collar north america 46-60
2 38 private 215646 9 divorced or separated single white male 0 0 40 <=50k secondary-high school graduate blue collar north america 36-45
3 53 private 234721 7 married male spouse black male 0 0 40 <=50k secondary blue collar north america 46-60
4 28 private 338409 13 married female spouse black female 0 0 40 <=50k tertiary white collar central america 26-35
5 37 private 284582 14 married female spouse white female 0 0 40 <=50k tertiary white collar north america 36-45
6 49 private 160187 5 divorced or separated single black female 0 0 16 <=50k secondary service central america 46-60
7 52 self-emp-not-inc 209642 9 married male spouse white male 0 0 45 >50k secondary-high school graduate white collar north america 46-60
8 31 private 45781 14 single single white female 14084 0 50 >50k tertiary white collar north america 26-35
9 42 private 159449 13 married male spouse white male 5178 0 40 >50k tertiary white collar north america 36-45

Dataset Dimensions and Data Types

Here, we examine the structure of the dataset:

- There are *32,513* entries and *16* variables.
- The dataset includes both **numerical** (e.g., `age`, `hours_per_week`) and **categorical** variables (e.g., `sex`, `education_level`).

Understanding data types and null entries is essential before proceeding with analysis.

Code
adult_df.shape
(32513, 16)
Code
adult_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 32513 entries, 0 to 32512
Data columns (total 16 columns):
 #   Column              Non-Null Count  Dtype 
---  ------              --------------  ----- 
 0   age                 32513 non-null  int64 
 1   workclass           32513 non-null  object
 2   fnlwgt              32513 non-null  int64 
 3   education_num       32513 non-null  int64 
 4   marital_status      32513 non-null  object
 5   relationship        32513 non-null  object
 6   race                32513 non-null  object
 7   sex                 32513 non-null  object
 8   capital_gain        32513 non-null  int64 
 9   capital_loss        32513 non-null  int64 
 10  hours_per_week      32513 non-null  int64 
 11  income              32513 non-null  object
 12  education_level     32513 non-null  object
 13  occupation_grouped  32513 non-null  object
 14  native_region       32513 non-null  object
 15  age_group           32513 non-null  object
dtypes: int64(6), object(10)
memory usage: 4.0+ MB

Summary Statistics: Numerical Variables

This summary provides a snapshot of key distribution characteristics. We see that:

- Age ranges from 17 to 90, with a mean of 38.6 years. It is slightly right-skewed (positively skewed). While the average age is approximately 38.6 years, an examination of the percentiles reveals that the majority of individuals are clustered in the younger to middle-age range, with fewer observations in the older age brackets. This skewed age distribution might suggest labor force participation is concentrated in specific age groups, which could reflect broader demographic or economic realities.

- Capital gains/losses are highly skewed, with most values at 0 (the 75th percentile is 0). This indicates that a small number of individuals report very large gains or losses, especially evident in the capital gain variable which reaches up to $99,999. These variables act as proxies for wealth-related income that goes beyond regular wages or salaries. Individuals with non-zero values for capital gains or losses often represent a distinct socioeconomic subset of the population — typically more financially literate, or with access to investment assets. The stark inequality in their distributions mirrors real-world disparities in asset ownership and investment returns.

- The dataset has individuals working anywhere from 1 to 99 hours per week, with a median of 40. This aligns with the standard full-time work week in many countries (8 hours per day for 5 working days). The mean is slightly above that at 40.4 hours, suggesting a mild right skew, with a small subset of individuals working significantly longer hours. The mode is also 40, further reinforcing the prevalence of full-time work. A non-trivial number of individuals report working very few hours, possibly due to part-time work, unemployment, or semi-retirement. On the other extreme, some report working more than 45 hours per week, which may indicate multiple jobs, weekend-work, self-employment, or informal labor, and could reflect socioeconomic necessity.

Code
adult_df.describe()
age fnlwgt education_num capital_gain capital_loss hours_per_week
count 32513.000000 3.251300e+04 32513.000000 32513.000000 32513.000000 32513.000000
mean 38.590256 1.897942e+05 10.081629 1079.239812 87.432719 40.440962
std 13.638932 1.055788e+05 2.572015 7390.625650 403.243596 12.350184
min 17.000000 1.228500e+04 1.000000 0.000000 0.000000 1.000000
25% 28.000000 1.178330e+05 9.000000 0.000000 0.000000 40.000000
50% 37.000000 1.783560e+05 10.000000 0.000000 0.000000 40.000000
75% 48.000000 2.370510e+05 12.000000 0.000000 0.000000 45.000000
max 90.000000 1.484705e+06 16.000000 99999.000000 4356.000000 99.000000

Summary Statistics: Categorical Variables

`workclass`

The private sector dominates, employing ~69.7% of the population. The government sector (13.4%) and self-employment (11.2%) also make up substantial portions of the workforce. A small fraction is labeled as “unknown” (5.6%), which may correspond to missing or ambiguous data entries. Tiny proportions are voluntary (0.04%) or unemployed (0.02%), possibly underreported or underrepresented in the sample.

`marital_status`

Married individuals make up the largest group (46.1%), followed by those who are single (32.8%) and divorced or separated (18.1%). Widowed individuals represent a small minority (~3.1%).

`relationship`

The majority are labeled as “male spouse” (40.5%) or “single” (36.1%). Smaller categories include children (15.6%), female spouses (4.8%), and extended relatives (3.0%). The dominance of `male spouse` reflects the dataset’s gendered structure and may point to traditional family roles. The relative scarcity of “female spouse” roles suggests potential gender imbalances in how income-earning is reported within households.

`race`

The dataset is overwhelmingly composed of White individuals (~85.4%). Other racial groups include Black (9.6%), Asian or Pacific Islander (3.2%), American Indian or Eskimo (1.0%), and Other (0.8%). The racial imbalance limits the generalizability of models trained on this data. Smaller racial groups may suffer from limited statistical power, affecting fairness and performance in predictive modeling.

`sex`

Males constitute 66.9% of the dataset, with females making up the remaining 33.1%. This male-skewed distribution could be due to sampling (e.g., primary earners in households), workforce participation patterns, or reporting biases.

`education_level`

Secondary-school graduates form the largest educational group (~32%), highlighting the central role of high school completion in the labor force. Tertiary education holders — those with university or equivalent degrees — account for nearly 25% of the population, representing a substantial segment with advanced qualifications. A notable 22.4% have attended some college without necessarily earning a degree, suggesting that partial post-secondary education is common, yet may not always translate into formal certification. The remaining 20% are distributed among those with only secondary education (9.4%), associate degrees (7.5%), primary school (3.5%), and a very small group with only preschool education (0.15%). It is ecident that the education distribution is skewed toward mid- to high-level education, with relatively few individuals having only basic schooling. This reflects a dataset that largely captures working-age adults in formal labor, which may underrepresent the least-educated populations.

`occupation_grouped`

White-collar occupations are the most prevalent (~51%), followed by blue-collar, service, and unknown. Smaller categories include military, which is marginal. Essentially, slightly over half of individuals in the dataset work in professional, managerial, sales, clerical, or tech-support roles. This suggests the dataset is heavily weighted toward professional and administrative occupations. Nearly a third of the population works in manual labor or skilled trade positions (craft, transport, machine operation, farming, etc.). This indicates a significant segment engaged in physically intensive or technical labor.

`native_region`

The vast majority of individuals are from North America (~92.3%). Smaller proportions are from Central America, Asia, Europe, South America, and a generic Other category. The heavy concentration of North American individuals reflects the U.S. focus of the dataset.

`age_group`

The largest groups are 26–35 and 36–45, followed by 46–60. These three age groups represent about 73% of the dataset. Very few individuals are under 18 or above 75, consistent with the dataset’s focus on the working-age population.

Code
adult_df.describe(include='object')
workclass marital_status relationship race sex income education_level occupation_grouped native_region age_group
count 32513 32513 32513 32513 32513 32513 32513 32513 32513 32513
unique 8 4 5 5 2 2 7 5 6 7
top private married male spouse white male <=50k secondary-high school graduate white collar north america 26-35
freq 22650 14984 13178 27771 21758 24677 10484 16532 30018 8501
Code
adult_df['workclass'].value_counts(normalize=True)
workclass
private             0.696644
self-emp-not-inc    0.078123
government          0.069418
local-gov           0.064374
unknown             0.056470
self-employed       0.034325
voluntary           0.000431
unemployed          0.000215
Name: proportion, dtype: float64
Code
adult_df['marital_status'].value_counts(normalize=True)
marital_status
married                  0.460862
single                   0.327684
divorced or separated    0.180912
widowed                  0.030542
Name: proportion, dtype: float64
Code
adult_df['relationship'].value_counts(normalize=True)
relationship
male spouse          0.405315
single               0.360686
child                0.155599
female spouse        0.048227
extended relative    0.030173
Name: proportion, dtype: float64
Code
adult_df['race'].value_counts(normalize=True)
race
white                        0.854151
black                        0.096023
asian or pacific islander    0.031926
american indian or eskimo    0.009565
other                        0.008335
Name: proportion, dtype: float64

Income Distribution

Given that `income` is the target variable, most of the analysis hereafter will be based on it. We first of all examine the income distribution in the dataset.

This pie chart visualizes the overall income split: 76% of individuals earn ≤50K, while 24% earn >50K. This means that nearly 3 out of 4 individuals fall into the lower income bracket (<=50K). This shows that there is a significant imbalance.

The bar chart visualizes the income distribution across age groups, using percentages within each group. There is an evident pattern in terms of income progression over the years with a gradual increase in terms of the number of people earning >50K starting from 0 amongst those aged 18 and below, peaking between 36 and 60 years, then declining after 60 years but not to zero.

All individuals under 18 earn <=50K, likely due to being students, minors, or ineligible for full-time employment. Extremely few young adults (2.1%) exceed 50K, as most are early in their careers, pursuing education, or in entry-level jobs. For the 26-35 age group, there’s a noticeable improvement — roughly 1 in 5 individuals in this group earn >50K, reflecting early career progression and accumulation of qualifications/experience. A substantial income increase is seen in the 36-45 age group: over a third now earn >50K. This is typically considered prime earning age where individuals settle into stable, higher-paying positions. Highest proportion of >50K earners is seen amongst individuals aged between 46 and 60— nearly 4 in 10. This reflects career maturity, peak seniority levels, and accumulated experience. There’s a drop-off in high incomes as many transition to retirement, part-time, or less demanding roles in the age group 61-75. Yet about 1 in 4 still earn >50K. Most in 76+ age group earn <=50K, likely due to retirement, pensions, or fixed incomes — but a small minority still earn higher incomes, possibly through continued work or investments.

Asia (30.7%) and Europe (29.2%) have the highest proportions of high-income earners. This suggests these immigrant groups might be better integrated into high-paying professional roles, or may represent a more skilled migrant profile in the dataset. Central America (11.1%) and South America (12.1%) have the lowest proportions of >50K earners. With 24.2% of North Americans earning >50K, this serves as a middle-ground baseline. Interestingly, both Asian and European groups outperform the native-born population proportionally in high-income brackets. The ‘Other’ group sits around 25.1%, close to North America’s rate. This likely reflects a diverse mix of regions not explicitly listed.

Code
adult_df_income =adult_df.groupby('income').size().reset_index(name='total')
adult_df_income
income total
0 <=50k 24677
1 >50k 7836
Code
pip install --upgrade plotly
Requirement already satisfied: plotly in c:\users\user\anaconda3\lib\site-packages (6.1.2)
Requirement already satisfied: narwhals>=1.15.1 in c:\users\user\anaconda3\lib\site-packages (from plotly) (1.43.1)
Requirement already satisfied: packaging in c:\users\user\anaconda3\lib\site-packages (from plotly) (23.1)
Note: you may need to restart the kernel to use updated packages.
Code
fig = px.pie(adult_df_income,
             names='income',
             values='total',
             title='Overall Income Distribution',
             color_discrete_sequence=px.colors.sequential.RdBu)

fig.update_layout(template="presentation",
                  paper_bgcolor="rgba(0,0,0,0)",
                  plot_bgcolor="rgba(0,0,0,0)") 

fig.show()
fig.write_image(os.path.join(results_dir, 'income_distribution_pie_chart.jpg'))
fig.write_image(os.path.join(results_dir, 'income_distribution_pie_chart.png'))
html_str = fig.to_html()

with open(os.path.join(results_dir, 'income_distribution_pie_chart.html'), 'w', encoding='utf-8') as f:
    f.write(html_str)

Income by Age Group

Code
adult_df_income_age = adult_df.groupby(['age_group', 'income']).size().reset_index(name='total_by_age').sort_values(['age_group', 'income'])
adult_df_income_age
age_group income total_by_age
0 18-25 <=50k 5333
1 18-25 >50k 114
2 26-35 <=50k 6910
3 26-35 >50k 1591
4 36-45 <=50k 5230
5 36-45 >50k 2771
6 46-60 <=50k 4479
7 46-60 >50k 2809
8 61-75 <=50k 1580
9 61-75 >50k 511
10 76+ <=50k 200
11 76+ >50k 40
12 <18 <=50k 945
Code
total_per_group = adult_df_income_age.groupby('age_group').size()
total_per_group
age_group
18-25    2
26-35    2
36-45    2
46-60    2
61-75    2
76+      2
<18      1
dtype: int64
Code
total_per_group = adult_df_income_age.groupby('age_group')['total_by_age'].transform('sum')
total_per_group
0     5447
1     5447
2     8501
3     8501
4     8001
5     8001
6     7288
7     7288
8     2091
9     2091
10     240
11     240
12     945
Name: total_by_age, dtype: int64
Code
total_per_group = adult_df_income_age.groupby('age_group')['total_by_age'].transform('sum')
adult_df_income_age['percentage'] = (adult_df_income_age['total_by_age']/total_per_group) * 100
adult_df_income_age
age_group income total_by_age percentage
0 18-25 <=50k 5333 97.907105
1 18-25 >50k 114 2.092895
2 26-35 <=50k 6910 81.284555
3 26-35 >50k 1591 18.715445
4 36-45 <=50k 5230 65.366829
5 36-45 >50k 2771 34.633171
6 46-60 <=50k 4479 61.457190
7 46-60 >50k 2809 38.542810
8 61-75 <=50k 1580 75.561932
9 61-75 >50k 511 24.438068
10 76+ <=50k 200 83.333333
11 76+ >50k 40 16.666667
12 <18 <=50k 945 100.000000
Code
fig = px.bar(
    adult_df_income_age,
    x='age_group',
    y='total_by_age',   
    color='income',
    title='Income Distribution by Age Group (%)',
    barmode='group',
    color_discrete_sequence=px.colors.sequential.RdBu,
    text='percentage'
)
fig.update_traces(texttemplate='%{text..2f}%', textposition='outside'),
fig.update_layout(template="presentation", xaxis_title='Age Group',
                  yaxis_title='Percentage of population', legend_title=dict(text='Income Level'),
                  paper_bgcolor = "rgba(0, 0, 0, 0)", plot_bgcolor = "rgba(0, 0, 0, 0)")
fig.show()
fig.write_image(os.path.join(results_dir, 'income_distribution_by_agegroup_bar_plot.jpg'))
fig.write_image(os.path.join(results_dir, 'income_distribution_by_agegroup_bar_plot.png'))
html_str = fig.to_html()

with open(os.path.join(results_dir, 'income_distribution_by_agegroup_bar_plot.html'), 'w', encoding='utf-8') as f:
    f.write(html_str)
Code
themes = ["plotly", "plotly_white", "plotly_dark", "ggplot2", "seaborn", "simple_white", "presentation", "xgridoff", "ygridoff", "gridon", "none"]

for theme in themes:
    fig.update_layout(template=theme)
    fig.show()
Code
#pip install -U kaleido
Code
#pip install -U plotly

Income by Native Region

Code
adult_df_income_native_region = adult_df.groupby(['native_region', 'income']).size().reset_index(name='total_income_distribution')
adult_df_income_native_region
native_region income total_income_distribution
0 asia <=50k 465
1 asia >50k 206
2 central america <=50k 466
3 central america >50k 58
4 europe <=50k 369
5 europe >50k 152
6 north america <=50k 22768
7 north america >50k 7250
8 other <=50k 435
9 other >50k 146
10 south america <=50k 174
11 south america >50k 24
Code
total_per_group = adult_df_income_native_region.groupby('native_region').size()
total_per_group
native_region
asia               2
central america    2
europe             2
north america      2
other              2
south america      2
dtype: int64
Code
total_per_group = adult_df_income_native_region.groupby('native_region')['total_income_distribution'].transform('sum')
total_per_group
0       671
1       671
2       524
3       524
4       521
5       521
6     30018
7     30018
8       581
9       581
10      198
11      198
Name: total_income_distribution, dtype: int64
Code
total_per_group = adult_df_income_native_region.groupby('native_region')['total_income_distribution'].transform('sum')
adult_df_income_native_region['percentage'] = (adult_df_income_native_region['total_income_distribution']/total_per_group) * 100
adult_df_income_native_region
native_region income total_income_distribution percentage
0 asia <=50k 465 69.299553
1 asia >50k 206 30.700447
2 central america <=50k 466 88.931298
3 central america >50k 58 11.068702
4 europe <=50k 369 70.825336
5 europe >50k 152 29.174664
6 north america <=50k 22768 75.847825
7 north america >50k 7250 24.152175
8 other <=50k 435 74.870912
9 other >50k 146 25.129088
10 south america <=50k 174 87.878788
11 south america >50k 24 12.121212
Code
fig = px.bar(
    adult_df_income_native_region,
    x='native_region',
    y='percentage',
    color='income',
    title='Income Distribution by Native Region (%)',
    barmode='group',
    height=500,
    color_discrete_sequence=px.colors.sequential.RdBu,
    text='percentage'
)


fig.update_traces(
    texttemplate='%{text:.2f}%',
    textposition='outside'  
)

fig.update_layout(
    template="presentation",
    xaxis_title='Native Region',
    yaxis_title='Percentage of Population',
    legend_title_text='Income Level',
    xaxis_title_standoff=30,
    margin=dict(l=50, r=50, t=50, b=50)
)

fig.show()
fig.write_image(os.path.join(results_dir, 'income_distribution_by_Native Region_bar_plot.jpg'))
fig.write_image(os.path.join(results_dir, 'income_distribution_by_Native Region_bar_plot.png'))
html_str = fig.to_html()

with open(os.path.join(results_dir, 'income_distribution_by_Native Region_bar_plot.html'), 'w', encoding='utf-8') as f:
    f.write(html_str)

Income by Race

Code
adult_df_income_race = adult_df.groupby(['race', 'income']).size().reset_index(name='total_by_race')
adult_df_income_race
race income total_by_race
0 american indian or eskimo <=50k 275
1 american indian or eskimo >50k 36
2 asian or pacific islander <=50k 762
3 asian or pacific islander >50k 276
4 black <=50k 2735
5 black >50k 387
6 other <=50k 246
7 other >50k 25
8 white <=50k 20659
9 white >50k 7112
Code
total_per_group = adult_df_income_race.groupby('race').size()
total_per_group
race
american indian or eskimo    2
asian or pacific islander    2
black                        2
other                        2
white                        2
dtype: int64
Code
total_per_group = adult_df_income_race.groupby('race')['total_by_race'].transform('sum')
total_per_group
0      311
1      311
2     1038
3     1038
4     3122
5     3122
6      271
7      271
8    27771
9    27771
Name: total_by_race, dtype: int64
Code
total_per_group = adult_df_income_race.groupby('race')['total_by_race'].transform('sum')
adult_df_income_race['percentage'] = (adult_df_income_race['total_by_race']/total_per_group) * 100
adult_df_income_race
race income total_by_race percentage
0 american indian or eskimo <=50k 275 88.424437
1 american indian or eskimo >50k 36 11.575563
2 asian or pacific islander <=50k 762 73.410405
3 asian or pacific islander >50k 276 26.589595
4 black <=50k 2735 87.604100
5 black >50k 387 12.395900
6 other <=50k 246 90.774908
7 other >50k 25 9.225092
8 white <=50k 20659 74.390551
9 white >50k 7112 25.609449
Code
fig = px.bar(
    adult_df_income_race,
    x = 'race',
    y = 'percentage',
    color = 'income',
    title='Income Distribution Per Race ',
    barmode='group',
    color_discrete_sequence=px.colors.sequential.RdBu,
    text='percentage'
)
fig.update_traces(texttemplate = '%{text:.2f}%')
fig.show()
fig.write_image(os.path.join(results_dir, 'income_distribution_by_Race_bar_plot.jpg'))
fig.write_image(os.path.join(results_dir, 'income_distribution_by_Race_bar_plot.png'))
html_str = fig.to_html()

with open(os.path.join(results_dir, 'income_distribution_by_Race_bar_plot.html'), 'w', encoding='utf-8') as f:
    f.write(html_str)

Education and occupation

Code
adult_df_income_edu_occ = (adult_df.groupby(['education_level', 'occupation_grouped', 'income'])
                          .size().reset_index(name='total').sort_values('total', ascending = False))
adult_df_income_edu_occ
education_level occupation_grouped income total
44 secondary-high school graduate blue collar <=50k 3976
63 tertiary white collar >50k 3545
62 tertiary white collar <=50k 3369
32 same college white collar <=50k 3003
52 secondary-high school graduate white collar <=50k 2900
... ... ... ... ...
21 primary unknow >50k 4
14 preschool white collar <=50k 3
37 secondary military >50k 2
11 preschool military <=50k 2
19 primary service >50k 1

64 rows × 4 columns

Code
adult_df_income_edu_occ['edu_occ'] = (adult_df_income_edu_occ['education_level'] + " | "
                                     + adult_df_income_edu_occ['occupation_grouped'])
adult_df_income_edu_occ
education_level occupation_grouped income total edu_occ
44 secondary-high school graduate blue collar <=50k 3976 secondary-high school graduate | blue collar
63 tertiary white collar >50k 3545 tertiary | white collar
62 tertiary white collar <=50k 3369 tertiary | white collar
32 same college white collar <=50k 3003 same college | white collar
52 secondary-high school graduate white collar <=50k 2900 secondary-high school graduate | white collar
... ... ... ... ... ...
21 primary unknow >50k 4 primary | unknow
14 preschool white collar <=50k 3 preschool | white collar
37 secondary military >50k 2 secondary | military
11 preschool military <=50k 2 preschool | military
19 primary service >50k 1 primary | service

64 rows × 5 columns

Code
num = 15
fig = px.bar(
    adult_df_income_edu_occ.head(15),
    x = 'total',
    y = 'edu_occ',
    color = 'income',
    orientation = 'h',
    title = f'Top{num} Education and Occupation Groups Combinations by Income Group',
    #barmode = 'group',
    height = 700,
    width=1100,
    color_discrete_sequence=px.colors.sequential.RdBu,
    text = 'total'
)

fig.update_layout(template="presentation", xaxis_title='Number of Individuals',
                  yaxis_title='Education | Occupation Group',
                  legend_title=dict(text='Income Level'),
                margin=dict(l=450, r=50, t= 50, b=50))
fig.update_traces(textposition='inside')
    
fig.show()
fig.write_image(os.path.join(results_dir, 'income_distribution_by_Education and Occupation_bar_plot.jpg'))
fig.write_image(os.path.join(results_dir, 'income_distribution_by_Education and Occupation_bar_plot.png'))
html_str = fig.to_html()

with open(os.path.join(results_dir, 'income_distribution_by_Education and Occupation_bar_plot.html'), 'w', encoding='utf-8') as f:
    f.write(html_str)